Given a sequence of item representations derived from the vector-quantized item codes, we use a sequential encoder to obtain the sequence representation. Following SASRec [1]}, we adopt a widely-used self-attentive Transformer architecture [2]}.
In detail, a typical Transformer encoder consists of stacks of multi-head self-attention layers (denoted by \(\operatorname{Attn}(\cdot )\) ) and multilayer perceptron networks (denoted by \(\operatorname{FFN}(\cdot )\) ). The index representations \({v}_i\)  (Eqn. (REF )) and absolute position embeddings \({p}_j\)  are summed up as input of Transformer encoder at position \(j\) . Then the update process can be formally written as:

fj0 = vi + pj,
